Apparatus and method for estimating impacts of operational problems in advanced control operations for industrial control systems

ABSTRACT

A method includes obtaining data associated with operation of a model-based industrial process controller. The method also includes identifying at least one estimated impact of at least one operational problem of the industrial process controller, where each estimated impact is expressed in terms of a lost opportunity associated with operation of the industrial process controller. The method further includes presenting the at least one estimated impact to a user. The at least one estimated impact could include impacts associated with noise or variance in process variables used by the industrial process controller, misconfiguration of an optimizer in the industrial process controller, one or more limits on one or more process variables, a quality of at least one model used by the industrial process controller, a quality of one or more inferred properties used by the industrial process controller, or one or more process variables being dropped from use by the industrial process controller.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY CLAIM

This application claims priority under 35 U.S.C. § 119(e) to thefollowing U.S. provisional patent applications:

U.S. Provisional Patent Application No. 62/518,352 filed on Jun. 12,2017;

U.S. Provisional Patent Application No. 62/518,397 filed on Jun. 12,2017;

U.S. Provisional Patent Application No. 62/518,474 filed on Jun. 12,2017; and

U.S. Provisional Patent Application No. 62/518,478 filed on Jun. 12,2017. All of these provisional applications are hereby incorporated byreference in their entirety.

TECHNICAL FIELD

This disclosure relates generally to industrial process control andautomation systems. More specifically, this disclosure relates to anapparatus and method for estimating impacts of operational problems inadvanced control operations for industrial control systems.

BACKGROUND

Industrial process control and automation systems are often used toautomate large and complex industrial processes. These types of controland automation systems routinely include process controllers and fielddevices like sensors and actuators. Some of the process controllerstypically receive measurements from the sensors and generate controlsignals for the actuators.

Model-based industrial process controllers are one type of processcontroller routinely used to control the operations of industrialprocesses. Model-based process controllers typically use one or moremodels to mathematically represent how one or more properties within anindustrial process respond to changes made to the industrial process.Unfortunately, the benefits that can be obtained using model-basedcontrollers often decline over time. This can be due to a number offactors, such as inaccurate models, misconfiguration, or operatoractions. In some extreme cases, the benefits that could be obtainedusing model-based controllers can be reduced by up to fifty percent oreven more over time.

SUMMARY

This disclosure provides an apparatus and method for estimating impactsof operational problems in advanced control operations for industrialcontrol systems.

In a first embodiment, a method includes obtaining data associated withoperation of a model-based industrial process controller. The methodalso includes identifying at least one estimated impact of at least oneoperational problem of the industrial process controller, where eachestimated impact is expressed in terms of a lost opportunity associatedwith operation of the industrial process controller. The method furtherincludes presenting the at least one estimated impact to a user.

In a second embodiment, an apparatus includes at least one interfaceconfigured to receive data associated with operation of a model-basedindustrial process controller. The apparatus also includes at least oneprocessor configured to identify at least one estimated impact of atleast one operational problem of the industrial process controller andto present the at least one estimated impact to a user. Each estimatedimpact is expressed in terms of a lost opportunity associated withoperation of the industrial process controller.

In a third embodiment, a non-transitory computer readable mediumcontains instructions that when executed cause at least one processingdevice to obtain data associated with operation of a model-basedindustrial process controller. The medium also contains instructionsthat when executed cause the at least one processing device to identifyat least one estimated impact of at least one operational problem of theindustrial process controller, where each estimated impact is expressedin terms of a lost opportunity associated with operation of theindustrial process controller. The medium further contains instructionsthat when executed cause the at least one processing device to presentthe at least one estimated impact to a user.

Other technical features may be readily apparent to one skilled in theart from the following figures, descriptions, and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of this disclosure, reference is nowmade to the following description, taken in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example industrial process control and automationsystem according to this disclosure;

FIG. 2 illustrates an example device for estimating impacts in advancedcontrol operations for industrial control systems according to thisdisclosure;

FIG. 3 illustrates an example method for estimating impacts in advancedcontrol operations for industrial control systems according to thisdisclosure;

FIGS. 4A and 4B illustrate an example method for estimating impacts ofconstraint issues, optimizer configuration issues, and processvariability issues in advanced control operations according to thisdisclosure;

FIG. 5 illustrates an example method for estimating impacts of modelquality issues in advanced control operations according to thisdisclosure;

FIG. 6 illustrates an example method for estimating impacts ofinferential quality issues in advanced control operations according tothis disclosure; and

FIG. 7 illustrates an example method for estimating impacts of processvariable mode issues in advanced control operations according to thisdisclosure.

DETAILED DESCRIPTION

FIGS. 1 through 7, discussed below, and the various embodiments used todescribe the principles of the present invention in this patent documentare by way of illustration only and should not be construed in any wayto limit the scope of the invention. Those skilled in the art willunderstand that the principles of the invention may be implemented inany type of suitably arranged device or system.

As noted above, model-based industrial process controllers are one typeof process controller routinely used to control the operations ofindustrial processes. Model-based process controllers can help toimprove the performance of continuous or other industrial processes. Forexample, in industrial processes, a controlled variable (CV) generallyrepresents a process variable that can be measured or inferred and thatis ideally controlled to be at or near a desired setpoint or within adesired range of values. A manipulated variable (MV) generallyrepresents a process variable that can be adjusted in order to alter oneor more controlled variables. A disturbance variable (DV) generallyrepresents a process variable whose value can be considered but cannotbe controlled. As a simple example, a flow rate of material through apipe could denote a controlled variable, a valve opening for a valvethat controls the flow rate of material could denote a manipulatedvariable, and an ambient temperature around the pipe or the valve coulddenote a disturbance variable.

The implementation of model-based control often requires substantialinvestments of time, money, and effort. Even with high initial costs,however, the general expectation is that these costs can be recoupedthrough use of these types of controllers within months if not weeks ofimplementation. Sustaining the performance of model-based controllerscan be important for realizing the benefits that these types ofcontrollers promise. Unfortunately, the performance of a model-basedcontroller often deteriorates over time due to a number of factors.These factors include equipment degradation, changes in the operationsof the process and the controller, operator actions, andartificially-constrained operating conditions. Without propermaintenance, model-based controllers may fail to provide any benefitsand fall into disuse after some time.

Maintenance of model-based controllers often requires experienced andtrained engineers, who can be in short supply. It is also oftendifficult for a few control engineers to maintain all of the model-basedcontrollers at an entire site. Monitoring applications are increasinglybeing used to help control engineers detect problems quickly anddiagnose probable causes. These applications provide key performanceindicators (KPIs) to help the control engineers detect problems.However, even with conventional model-based controllers and monitoringtools, various industries still face a number of challenges. Forexample, many of the key performance indicators in conventionalmonitoring tools are very technical and require additional skills tounderstand and interpret in order to take effective actions. Also, withskilled personnel in short supply, it is often useful or important toprioritize issues in terms of financial impacts, but current monitoringtools do not provide this insight. In addition, it may be difficult forprocess operations teams to understand model-based controllers andmonitoring tools and to contribute and participate in maintaining thecontrollers, and it may be difficult for operations managers to provideessential oversight.

This disclosure provides various techniques for identifying the impactsof operational problems involving a model-based controller in anindustrial control and automation system. The impacts here can bemeasured against the maximum benefit potential that could be obtainedusing the model-based controller. In other words, the impacts canrepresent measures of how the current operation of the model-basedcontroller falls below the theoretical best operation of the model-basedcontroller. The impacts can be expressed in various ways. In someembodiments, the impacts are expressed in terms of economic impacts,such as monetary losses. An impact can be referred to as a “cost of lostopportunity” in this document, meaning the impact measures the cost ofnot operating the model-based controller at its full potential. Ofcourse, the impacts from not operating the model-based controller at itsfull potential could be expressed in other ways, such as excess materialusage, excess energy usage, or reduced product production. The “cost”referred to in a “cost of lost opportunity” can also serve as a measureof the excess material usage, excess energy usage, reduced productproduction, or other impact.

In this way, these techniques help to identify different operationalissues that can affect a model-based controller and the impacts of thoseoperational issues. Whether expressed in material or energy usage,product production, economic terms, or other terms, the impacts of theoperational issues can be ranked or otherwise used to identify issuesthat affect the model-based controller and that might need furtherinvestigation or resolution. The impacts can also be used to justifysuch investigation or resolution, such as when the costs of the economicor other impacts outweigh the costs of the investigation or resolution.

Among other things, this could enable a new Industrial Internet ofThings (IIoT) service or other service to be deployed, where the servicecan be used to reduce the cost of troubleshooting a model-basedcontroller's performance and to improve the lifecycle benefits of themodel-based controller. In particular embodiments, these techniquescould be implemented using a computer program that periodically analysesbatches of data collected from customers' premises as part of acloud-based analytics solution. The resulting analysis conclusions couldthen be visualized to the customers using cloud-hosted dashboards toenable the customers, support engineering teams, or other personnel toview performance information and troubleshoot performance issues. Ofcourse, other implementations of the described functionality could alsobe used.

FIG. 1 illustrates an example industrial process control and automationsystem 100 according to this disclosure. As shown in FIG. 1, the system100 includes various components that facilitate production or processingof at least one product or other material. For instance, the system 100can be used to facilitate control over components in one or multipleindustrial plants. Each plant represents one or more processingfacilities (or one or more portions thereof), such as one or moremanufacturing facilities for producing at least one product or othermaterial. In general, each plant may implement one or more industrialprocesses and can individually or collectively be referred to as aprocess system. A process system generally represents any system orportion thereof configured to process one or more products or othermaterials in some manner.

In FIG. 1, the system 100 includes one or more sensors 102 a and one ormore actuators 102 b. The sensors 102 a and actuators 102 b representcomponents in a process system that may perform any of a wide variety offunctions. For example, the sensors 102 a could measure a wide varietyof characteristics in the process system, such as flow, pressure, ortemperature. Also, the actuators 102 b could alter a wide variety ofcharacteristics in the process system, such as valve openings. Each ofthe sensors 102 a includes any suitable structure for measuring one ormore characteristics in a process system. Each of the actuators 102 bincludes any suitable structure for operating on or affecting one ormore conditions in a process system.

At least one network 104 is coupled to the sensors 102 a and actuators102 b. The network 104 facilitates interaction with the sensors 102 aand actuators 102 b. For example, the network 104 could transportmeasurement data from the sensors 102 a and provide control signals tothe actuators 102 b. The network 104 could represent any suitablenetwork or combination of networks. As particular examples, the network104 could represent at least one Ethernet network (such as onesupporting a FOUNDATION FIELDBUS protocol), electrical signal network(such as a HART network), pneumatic control signal network, or any otheror additional type(s) of network(s).

The system 100 also includes various controllers 106. The controllers106 can be used in the system 100 to perform various functions in orderto control one or more industrial processes. For example, a first set ofcontrollers 106 may use measurements from one or more sensors 102 a tocontrol the operation of one or more actuators 102 b. A second set ofcontrollers 106 could be used to optimize the control logic or otheroperations performed by the first set of controllers. A third set ofcontrollers 106 could be used to perform additional functions. Thecontrollers 106 could therefore support a combination of approaches,such as regulatory control, advanced regulatory control, supervisorycontrol, and advanced process control.

Each controller 106 includes any suitable structure for controlling oneor more aspects of an industrial process. At least some of thecontrollers 106 could, for example, representproportional-integral-derivative (PID) controllers or multivariablecontrollers, such as controllers implementing model predictive control(MPC) or other advanced predictive control (APC). As a particularexample, each controller 106 could represent a computing device runninga real-time operating system, a WINDOWS operating system, or otheroperating system.

At least one of the controllers 106 shown in FIG. 1 could denote amodel-based controller that operates using one or more process models.For example, each of these controllers 106 could operate using one ormore process models to determine, based on measurements from one or moresensors 102 a, how to adjust one or more actuators 102 b. In someembodiments, each model associates one or more manipulated ordisturbance variables (often referred to as independent variables) withone or more controlled variables (often referred to as dependentvariables). Each of these controllers 106 could use an objectivefunction to identify how to adjust its manipulated variables in order topush its controlled variables to the most attractive set of constraints.

At least one network 108 couples the controllers 106 and other devicesin the system 100. The network 108 facilitates the transport ofinformation between to components. The network 108 could represent anysuitable network or combination of networks. As particular examples, thenetwork 108 could represent at least one Ethernet network.

Operator access to and interaction with the controllers 106 and othercomponents of the system 100 can occur via various operator consoles110. Each operator console 110 could be used to provide information toan operator and receive information from an operator. For example, eachoperator console 110 could provide information identifying a currentstate of an industrial process to the operator, such as values ofvarious process variables and warnings, alarms, or other statesassociated with the industrial process. Each operator console 110 couldalso receive information affecting how the industrial process iscontrolled, such as by receiving setpoints or control modes for processvariables controlled by the controllers 106 or other information thatalters or affects how the controllers 106 control the industrialprocess. Each operator console 110 includes any suitable structure fordisplaying information to and interacting with an operator. For example,each operator console 110 could represent a computing device running aWINDOWS operating system or other operating system.

Multiple operator consoles 110 can be grouped together and used in oneor more control rooms 112. Each control room 112 could include anynumber of operator consoles 110 in any suitable arrangement. In someembodiments, multiple control rooms 112 can be used to control anindustrial plant, such as when each control room 112 contains operatorconsoles 110 used to manage a discrete part of the industrial plant.

The control and automation system 100 here may optionally include atleast one historian 114 and/or one or more servers 116. The historian114 represents a component that stores various information about thesystem 100. The historian 114 could, for instance, store informationthat is generated by the various controllers 106 during the control ofone or more industrial processes. The historian 114 includes anysuitable structure for storing and facilitating retrieval ofinformation. Although shown as a single component here, the historian114 could be located elsewhere in the system 100, or multiple historianscould be distributed in different locations in the system 100.

Each server 116 denotes a computing device that executes applicationsfor users of the operator consoles 110 or other applications. Theapplications could be used to support various functions for the operatorconsoles 110, the controllers 106, or other components of the system100. Each server 116 could represent a computing device running aWINDOWS operating system or other operating system. Note that whileshown as being local within the control and automation system 100, thefunctionality of the server 116 could be remote from the control andautomation system 100. For instance, the functionality of the server 116could be implemented in a computing cloud 118 or a remote servercommunicatively coupled to the control and automation system 100 via agateway 120.

At least one component of the system 100 could support a mechanism forestimating impacts in advanced control operations for industrial controlsystems. For example, this functionality could be implemented in anoperator console 110, a server 116, or a computing cloud 118 or remoteserver. Among other things, this functionality can be used to evaluatedata associated with one or more model-based controllers 106 to identifyoperational issues with the controller(s) 106, and the impacts of theidentified operational issues can be determined. Visualizations can alsobe provided, such as on displays of the operator consoles 110, to helpusers identify the operational issues and their impacts. Ideally, thisallows the operational issues to be prioritized and reduced or resolved,which can help to improve the operation of the model-based controllers106. Additional details regarding this functionality are provided below.

There are various ways in which this functionality can be used in orderto support modifications to the operations of model-based controllers106 or underlying industrial processes. For example, in one example usecase, this functionality can be used to provide a view of the topimprovement opportunities for an industrial site, along with theassociated costs to operations or other impacts in decreasing order ofvalue. This allows site managers or other personnel to identify the bestpotential improvements that could be made to the industrial site. Inanother example use case, this functionality can be used to identify thelong-term performance of model-based controllers in terms of totalvalue, along with references to benchmark performance and maximumpossible performance. This allows engineers or other personnel toidentify controllers that are most in need of tuning or othermaintenance. As yet another example use case, this functionality can beused to identify actionable metrics, such as in terms of the cost oflost opportunity per controller. This allows engineers or otherpersonnel to focus on the most impactful problems. As still anotherexample use case, this functionality can be used to calculate the costof lost opportunity in a vendor-neutral way since these approaches canbe used with model-based multivariable controllers from differentsuppliers. This allows managers, engineers, or other personnel toevaluate controller performance across multiple vendors in a consistentway. Of course, the functionality described in this patent documentcould be used in any other suitable manner.

Although FIG. 1 illustrates one example of an industrial process controland automation system 100, various changes may be made to FIG. 1. Forexample, the system 100 could include any number of sensors, actuators,controllers, networks, operator consoles, control rooms, historians,servers, and other components. Also, the makeup and arrangement of thesystem 100 in FIG. 1 is for illustration only. Components could beadded, omitted, combined, further subdivided, or placed in any othersuitable configuration according to particular needs. As a particularexample, the historian 114 may be implemented in the computing cloud118. Further, particular functions have been described as beingperformed by particular components of the system 100. This is forillustration only. In general, control and automation systems are highlyconfigurable and can be configured in any suitable manner according toparticular needs. In addition, FIG. 1 illustrates one exampleoperational environment where impacts in advanced control operations forindustrial control systems can be estimated. This functionality can beused in any other suitable system.

FIG. 2 illustrates an example device 200 for estimating impacts inadvanced control operations for industrial control systems according tothis disclosure. The device 200 could, for example, denote an operatorconsole 110, server 116, or device used in the computing cloud 118described above with respect to FIG. 1. However, the device 200 could beused in any other suitable system.

As shown in FIG. 2, the device 200 includes at least one processor 202,at least one storage device 204, at least one communications unit 206,and at least one input/output (I/O) unit 208. Each processor 202 canexecute instructions, such as those that may be loaded into a memory210. The instructions could estimate the impacts in advanced controloperations as described in this patent document. Each processor 202denotes any suitable processing device, such as one or moremicroprocessors, microcontrollers, digital signal processors,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), or discrete circuitry.

The memory 210 and a persistent storage 212 are examples of storagedevices 204, which represent any structure(s) capable of storing andfacilitating retrieval of information (such as data, program code,and/or other suitable information on a temporary or permanent basis).The memory 210 may represent a random access memory or any othersuitable volatile or non-volatile storage device(s). The persistentstorage 212 may contain one or more components or devices supportinglonger-term storage of data, such as a read only memory, hard drive,Flash memory, or optical disc.

The communications unit 206 supports communications with other systemsor devices. For example, the communications unit 206 could include anetwork interface card or a wireless transceiver facilitatingcommunications over a wired or wireless network. The communications unit206 may support communications through any suitable physical or wirelesscommunication link(s).

The I/O unit 208 allows for input and output of data. For example, theI/O unit 208 may provide a connection for user input through a keyboard,mouse, keypad, touchscreen, or other suitable input device. The I/O unit208 may also send output to a display, printer, or other suitable outputdevice.

Although FIG. 2 illustrates one example of a device 200 for estimatingimpacts in advanced control operations for industrial control systems,various changes may be made to FIG. 2. For example, components could beadded, omitted, combined, further subdivided, or placed in any othersuitable configuration according to particular needs. Also, computingdevices can come in a wide variety of configurations, and FIG. 2 doesnot limit this disclosure to any particular configuration of computingdevice.

FIG. 3 illustrates an example method 300 for estimating impacts inadvanced control operations for industrial control systems according tothis disclosure. For ease of explanation, the method 300 is described asbeing performed by the device 200 of FIG. 2 implementing an operatorconsole 110, server 116, or device used in the computing cloud 118 ofFIG. 1. However, the method 300 could be used with any suitable deviceand in any suitable system.

As shown in FIG. 3, a start and an end of an analysis period areidentified at step 302. This could include, for example, the processor202 of the device 200 determining the period during which dataassociated with a model-based controller 106 will be analyzed. Anysuitable period of time could be identified here, such as a particularday, week, month, or other period of time. Data associated with themodel-based controller is obtained at step 304. This could include, forexample, the processor 202 of the device 200 obtaining data identifyingprocess variable values associated with the controller 106 during theidentified analysis period. This could also include the processor 202 ofthe device 200 pre-processing the data, such as to validate the obtaineddata and discard any portions of the obtained data deemed invalid. Notethat when the data is obtained by a device that is remote from thecontroller 106 or other data source, the data can be transmitted to thedevice securely and in real-time, near real-time, or non-real-timedepending on the embodiment.

The data can be analyzed continuously, periodically, or at othersuitable times to identify at least one impact associated with one ormore operational problems affecting the controller at step 306. Thiscould include, for example, the processor 202 of the device 200analyzing the data to determine whether the model-based controller 106is suffering from any constraint/limit issues, model quality issues,inferential quality issues, variable mode issues, optimizerconfiguration issues, or process variable noise/variance issues. Theseissues are described in more detail below. In general, each operationalproblem that is identified can affect the operation of the model-basedcontroller 106 and prevent the controller 106 from achieving the mostbeneficial operation possible. This could also include the processor 202of the device 200 determining a measure of how much each of theidentified operational problems prevents the model-based controller 106from achieving the most beneficial operation possible. As noted above,the impacts could be expressed in any suitable terms, such as excessmaterial usage, excess energy usage, reduced product production, or losteconomic costs (and the lost economic costs could themselves be ameasure of things like excess material usage, excess energy usage, orreduced product production).

A graphical display identifying one or more impacts for one or more ofthe operational problems is generated and presented to one or more usersat step 308. This could include, for example, the processor 202 of thedevice 200 generating a graphical user interface that identifies theoperational problems and associated impacts. The operational problemscould be presented in any suitable manner, such as when ranked byincreasing or decreasing impacts.

Although FIG. 3 illustrates one example of a method 300 for estimatingimpacts in advanced control operations for industrial control systems,various changes may be made to FIG. 3. For example, while shown as aseries of steps, various steps in FIG. 3 could overlap, occur inparallel, occur in a different order, or occur any number of times.

The following describes specific types of operational issues that canaffect a model-based controller and how impacts of those specific typesof operational issues can be identified. Note, however, that theoperational issues and impacts described below are merely examples ofthe types of operational issues and impacts that could be identifiedusing the techniques disclosed in this patent document. Any other oradditional types of operational issues and impacts could be identifiedwithout departing from the scope of this disclosure.

In many instances, model-based controllers have the ability to considera large number of process variables when determining how to control anindustrial process. In order to identify which process variables toconsider when attempting to control the industrial process, amodel-based controller often attempts to solve an optimization problem.An example optimization problem that could be solved by a model-basedcontroller could be expressed as:

min½∥φ∥₂ ²  (1)

where:

φ=Σ_(i)α_(i)*(y _(i) −y _(io))²+β_(i) *y _(i)+Σ_(j)α_(j)*(u _(j) −u_(jo))²+β_(j) *u _(j)  (2)

subject to:

y _(li) ≤y _(i) ≤y _(hi)  (3)

u _(lj) ≤u _(j) ≤u _(hj)  (4)

Here, φ represents the computed value of the objective function. Also,y_(i) represents the current value of the i^(th) controlled variable,y_(i0) represents the ideal resting value for the i^(th) controlledvariable, y_(hi) represents the high limit (constraint) for the i^(th)controlled variable, and represents the low limit (constraint) for thei^(th) controlled variable. Further, u_(j) represents the current valueof the j^(th) manipulated variable, u_(j0) represents the ideal restingvalue for the j^(th) manipulated variable, u_(hj) represents the highlimit (constraint) for the j^(th) manipulated variable, and u_(ij)represents the low limit (constraint) for the j^(th) manipulatedvariable. In addition, α_(i) and α_(j) respectively represent quadraticcosts for the i^(th) controlled variable and the j^(th) manipulatedvariable, and β_(i) and β_(j) respectively represent linear costs forthe i^(th) controlled variable and the j^(th) manipulated variable.

As noted above, various issues can affect a model-based controller 106and result in reductions to the benefits of model-based control. As aresult, the benefits that can be obtained using a model-based controllercan fall over time (in some cases very significantly). The issues thatcan affect a model-based controller 106 include constraint/limit issues,model quality issues, inferential quality issues, variable mode issues,optimizer configuration issues, and process variable noise/varianceissues. Limit issues can arise due to incorrect or over-constrainedlimits placed on manipulated variables and/or controlled variables, suchas the y_(hi), y_(li), u_(hj), and u_(lj) limits noted above. Limitissues can alternatively arise due to equipment processing limits orother physical throughput limitations of the equipment itself.

Model quality issues can arise since industrial processes are oftennonlinear and time-variant, while models of industrial processes used bymodel-based controllers are typically Linear Time Invariant (LTI). Thus,a controller's dynamic response and a solution from the controller'sembedded optimizer can be affected when an industrial process drifts,such as due to changes in feed quality, operating points, ambientconditions, and other operating conditions. Also, inaccuracies in themodels when generated can degrade the controller's response andmisdirect the controller's optimizer.

Inferential quality issues can arise since soft sensors or inferredproperties are used quite frequently as controlled variables inmodel-based controllers as proxies for laboratory or analyzer values.Many inferred properties limits or inferred quality specifications arefrequently constraints to optimization. As a result, the quality of aninference can have a direct impact on the benefits that can be achievedby model-based controllers. Inferential quality issues can therefore becaused when the quality of inferred values is poor.

Variable mode issues can arise since some process variables may beexcluded or dropped from consideration by model-based controllers. Forexample, due to various transient issues, an operator may remove one ormore process variables from a controller's control matrix. This preventsthe controller 106 from considering those process variables during thecalculation of its objective function values or other optimizationcalculations. In some cases, process variables may be removed from thecontroller's control matrix for extended periods of time, and thecontroller's operation in such conditions can become sub-optimal.

Optimizer configuration issues can arise since, in many model-basedcontrollers, the “costs” of variables for optimization or objectivecoefficients may not represent actual real (monetary) costs. In somecases, these costs are entered and used merely to give directions to theprocess variables. Even when costs that are entered are real monetarycosts, they may not be updated with changes in market values or changesin modes of operation. As a result, this may cause the optimizer to nottarget a true optimum value in economic terms.

Process variable noise/variability issues can arise when the values ofprocess variables excessively change due to noise or other issues. Noiseor variance in controlled variables can be caused by various factors,such as measurement noise, underlying manipulated variable PIDcontroller oscillations, or high variance in measured or unmeasureddisturbance variables. In some cases when the controller 106 is tunedaggressively, high variance can also be due to mismatch between acontroller's model and the actual industrial process. Mismatch may alsobe due to nonlinearity, which can result in limit cycling of thecontroller 106. One impact of high noise and variance is that amodel-based controller 106 can push the average values of its controlledvariables away from their associated limits in order to maintain thecontrolled variables within their limits. It may be more ideal to pushthose controlled variables to their limits.

The following discussions of FIGS. 4A through 7 describe exampletechniques for identifying or measuring the impacts of these types ofoperational issues that may affect a model-based controller 106. Inthese examples, it is assumed that a “shadow” optimizer is available foruse. The shadow optimizer represents a “digital twin” of a model-basedcontroller's optimizer. In other words, a replica of the functionalityof the controller's optimizer can be used during the analysis. Theshadow optimizer can be executed by any suitable device within oroutside of a control and automation system, such as by an operatorconsole 110, server 116, or device used in the computing cloud 118described above with respect to FIG. 1.

In some embodiments, the data used by a shadow optimizer could besubstantially the same data used by the embedded optimizer in one ormore model-based controllers 106, and that data could have any suitablecollection frequency or frequencies. In other embodiments, the data usedby a shadow optimizer could be averaged data that is used by theembedded optimizer in one or more model-based controllers 106, such ashourly average values or data averaged at higher or lower frequencies.In addition to the controller data and gain matrix used by a model-basedcontroller 106, the following data could be provided to the shadowoptimizer. The prices of feeds, products, raw materials, and utilitiesat the boundary of an industrial site could be used, and the pricescould be sourced from an enterprise resource planning (ERP) applicationor any other suitable source(s). Also, the ideal limits of processvariables could be used, and the ideal limits could be sourced fromboundary management software (which defines an operating envelope), froman initial benefit study used to justify the model-based controller, orfrom any other suitable source(s).

In particular embodiments, when true prices for all product streams areavailable, the shadow optimizer could be set up as a Product ValueOptimizer (PVO). PVO refers to an optimization when true processeconomics are directly entered into the controller as either independentvariables or dependent variables. In other particular embodiments, theshadow optimizer could be augmented with additional controlled variablesif the original model-based controller 106 is not designed as a PVO. Inthe absence of true prices for all product streams, in some instancesthe shadow optimizer could be set up based on indicative costcoefficients for some units, such as when an operating margin per unitfeed can be used as an alternative cost coefficient.

FIGS. 4A and 4B illustrate an example method 400 for estimating impactsof constraint issues, optimizer configuration issues, and processvariability issues in advanced control operations according to thisdisclosure. The method 400 could, for example, be performed as part ofstep 306 in FIG. 3. For ease of explanation, the method 400 is describedas being performed by the device 200 of FIG. 2 implementing an operatorconsole 110, server 116, or device used in the computing cloud 118 ofFIG. 1. However, the method 400 could be used with any suitable deviceand in any suitable system.

As shown in FIGS. 4A and 4B, a current value (φ_(i)) of a model-basedcontroller's objective function is calculated at step 402. This couldinclude, for example, the processor 202 of the device 200 calculatingthe current objective function value using the shadow optimizer. Thecurrent value of the controller's objective function could be based on(i) one or more real prices for one or more products and (ii) data andlimits collected from an industrial site. Differences betweensteady-state and average values for one or more process variablesassociated with the model-based controller's objective function aredetermined at step 404. This could include, for example, the processor202 of the device 200 identifying the steady-state and average valuesfor each process variable contained in the controller's objectivefunction based on the data collected from the model-based controller106.

A first of the process variables is selected at step 406. This couldinclude, for example, the processor 202 of the device 200 identifyingthe first process variable associated with the controller's objectivefunction. The average value of the selected process variable is changedto equal the steady-state value of the selected process variable at step408, and a new value (φ_(s)) of the controller's objective function iscalculated at step 410. This could include, for example, the processor202 of the device 200 setting the average value of the selected processvariable to the process variable's steady-state value. This could alsoinclude the processor 202 of the device 200 using the shadow optimizerto calculate the value of the controller's objective function using thechanged average value of the selected process variable. If anotherprocess variable remains to be selected at step 412, the process returnsto step 406. In general, steps 406-412 iterate through a list of processvariables and sequentially change each process variable's average valueto its steady-state value. The results from these steps include asequence of φ_(s) values identifying how the value of the controller'sobjective function changed due to the process variable changes.

A difference between the φ_(i) value and the first φ_(s) value isidentified as the impact caused by variance of the first processvariable at step 414, and subsequent changes in the φ_(s) values areidentified as the impacts caused by variance of the subsequent processvariables at step 416. This could include, for example, the processor202 of the device 200 calculating the difference between the φ_(i) valueand the first φ_(s) value and using this value as the cost of lostopportunity or other impact for variance of the first process variableselected in step 406. This could also include the processor 202 of thedevice 200 calculating the changes between the φ_(s) values and usingthe changes as the costs of lost opportunity or other impacts forvariance of the sequence of process variables following the firstvariable selected in step 406.

A new value (φ_(o)) of the model-based controller's objective functionis calculated at step 418. This could include, for example, theprocessor 202 of the device 200 using the shadow optimizer to calculatethe value of the controller's objective function using all processvariables set to their steady-state values. A difference between theφ_(o) value and the φ_(i) value is identified as the impact caused byoptimizer configuration issues at step 420. This could include, forexample, the processor 202 of the device 200 calculating the differencebetween the φ_(o) value and the φ_(i) value and using this value as thecost of lost opportunity or other impact for the optimizer configurationissues. This value measures the combined effects of how the controller'soptimizer fails to allow its process variables' average values to reachtheir steady-state values.

A constraint limit (high or low) on one of the process variables isselected at step 422, and the constraint limit is perturbed or changedto its ideal limit at step 424. This could include, for example, theprocessor 202 of the device 200 selecting one of the current constraintsfor a process variable contained in the controller's objective functionand the associated ideal value for that constraint. As noted above, theideal value could be identified previously, such as from managementsoftware or an initial benefit study. A new value (φ_(k)) of thecontroller's objective function is calculated based on the updated limitat step 426. This could include, for example, the processor 202 of thedevice 200 using the shadow optimizer to calculate the value of thecontroller's objective function using the modified constraint value. Adifference between the φ_(k) value and the φ_(o) value is identified asthe impact caused by the selected constraint limit at step 428. Thiscould include, for example, the processor 202 of the device 200calculating the difference between the φ_(k) value and the φ_(o) valueand using this value as the cost of lost opportunity or other impact forthe selected constraint limit. The selected constraint limit is returnedto its original value at step 430. If any additional constraints for anyof the process variables remain to be processed at step 432, the methodreturns to step 422 to select another constraint.

It should be noted that during steps 422-432, the cost of lostopportunity or other impact for a selected constraint limit could becalculated assuming that the ideal value for the selected constraintlimit is not bound by existing equipment processing limits or otherphysical throughput limitations of the equipment itself. Thus, theimpact for the selected constraint limit could be used as an indicatorof whether larger or other non-constricting or less-constrictingequipment could be used in place of the existing equipment in order toobtain process improvements. This functionality can be referred to as“debottlenecking” since it aims to relax equipment processing limitsthat can act as bottlenecks in an industrial process.

The final results from the method 400 include an identification ofimpacts caused by variability of the process variables, impacts causedby the optimizer's configuration, and impacts caused by the controller'scurrent constraints. Of course, only a subset of these impacts may beneeded or desired in a particular implementation, in which case thecorresponding calculations in the method 400 could be omitted.

FIG. 5 illustrates an example method 500 for estimating impacts of modelquality issues in advanced control operations according to thisdisclosure. The method 500 could, for example, be performed as part ofstep 306 in FIG. 3. For ease of explanation, the method 500 is describedas being performed by the device 200 of FIG. 2 implementing an operatorconsole 110, server 116, or device used in the computing cloud 118 ofFIG. 1. However, the method 500 could be used with any suitable deviceand in any suitable system.

As shown in FIG. 5, model error associated with a model-based controlleris identified at step 502. This could include, for example, theprocessor 202 of the device 200 identifying the quality of one or moremodels used by the controller 106 to represent the behavior of at leastone underlying industrial process. In particular embodiments, this mayinclude the processor 202 of the device 200 identifying corrected gainsor gain multipliers using the techniques described in U.S. Pat. No.7,421,374 (which is hereby incorporated by reference in its entirety).Gains or gain multipliers obtained during the identification of themodel error are provided to the shadow optimizer at step 504. This couldinclude, for example, the processor 202 of the device 200 inserting thegains or gain multipliers into the control matrix of the shadowoptimizer.

A new value (φ_(m)) of the controller's objective function is calculatedat step 506. This could include, for example, the processor 202 of thedevice 200 using the shadow optimizer to calculate the value of thecontroller's objective function using the updated gains or gainmultipliers. A difference between the φ_(m) value and the coo value isidentified at step 508, and the difference is used as the impact causedby model quality issues at step 510. This could include, for example,the processor 202 of the device 200 calculating the difference betweenthe φ_(m) value and the φ_(o) value and using this value as the cost oflost opportunity or other impact for the model quality issues. Thisvalue measures the combined effects of how the controller's model failsto accurately match the actual behaviors of the underlying industrialprocess.

The final results from the method 500 include an identification ofimpacts caused by model quality issues associated with a model-basedcontroller's model(s). Note that during this process, the originalconstraint limits used by the controller 106 can be maintained (ratherthan being changed to their ideal limits) so that the quantification ofany model quality issues is independent of the quantification of thelimit issues (as identified in the method 400). Also, it is possible torepeat the analysis of the limit issues using the method 400 after thecorrected gains or gain multipliers are provided to the model-basedcontroller 106 so that any limit issues remaining after the gains orgain multipliers have been corrected can be identified.

FIG. 6 illustrates an example method 600 for estimating impacts ofinferential quality issues in advanced control operations according tothis disclosure. The method 600 could, for example, be performed as partof step 306 in FIG. 3. For ease of explanation, the method 600 isdescribed as being performed by the device 200 of FIG. 2 implementing anoperator console 110, server 116, or device used in the computing cloud118 of FIG. 1. However, the method 600 could be used with any suitabledevice and in any suitable system.

Inferential properties or soft sensors can have various issues, such ashigh variance and/or high bias. For example, many inferential propertiesare provided with updates from a laboratory or analyzer. When an updatedoes not occur, there may be no conventional way to figure out theissue(s) with the inferential properties or soft sensors. If generatedinferential measurements are not noisy and lack high variability of biasbetween laboratory and predicted values, successive bias updates cangradually reduce the offset between the laboratory and predicted values.If the generated inferential measurements have a high variability ofbias, it can result in high controlled variable variability. High biasvariability can be due to prediction variability or laboratoryvariability.

As shown in FIG. 6, differences between predicted and laboratory valuesfor one or more inferred properties are identified at step 602. Thiscould include, for example, the processor 202 of the device 200obtaining the predicted and laboratory values for each inferred propertyused in the controller's objective function. This data could be obtainedfrom any suitable source(s), such as directly from the model-basedcontroller 106 or indirectly, such as via a historian 114, server 116,or gateway 120. At least some of this data could also be obtained usingthe shadow optimizer. The length of time spanned by the data values herecould be an extended period of time, such as ten days or more.Statistics associated with the bias (the identified differences) arecalculated at step 604. This could include, for example, the processor202 of the device 200 calculating the average, standard deviation, orother descriptive statistics of the bias.

Possible average shifts to the various process variables used by thecontroller are identified using the bias statistics at step 606. Thiscould include, for example, the processor 202 of the device 200determining how much the controller 106 could move its process variablesaway from their average values towards their constraint limits. Exampletechniques for identifying possible shifts to the process variables aredescribed in the various provisional patent applications incorporated byreference above, as well as in U.S. patent application Ser. No. ______,[Docket No. H0060315-0114] entitled “APPARATUS AND METHOD FORIDENTIFYING IMPACTS AND CAUSES OF VARIABILITY OR CONTROL GIVEAWAY ONMODEL-BASED CONTROLLER PERFORMANCE” (filed concurrently herewith).

One of the process variables is selected at step 608. This couldinclude, for example, the processor 202 of the device 200 selecting oneof the process variables associated with the controller's objectivefunction. The selected process variable is shifted by the average shiftamount for that process variable at step 610. This could include, forexample, the processor 202 of the device 200 increasing or decreasingthe value of the selected process variable by the average shift amountidentified in the previous step so that the selected process variable isat or closer to its constraint.

A new value of the controller's objective function is calculated at step612. This could include, for example, the processor 202 of the device200 using the shadow optimizer to calculate the value of thecontroller's objective function using the shifted process variablevalue. A difference between the new value of the controller's objectivefunction and a prior value of the controller's objective function isidentified at step 614, and the difference is used as the impact causedby an inferential quality issue at step 616. This could include, forexample, the processor 202 of the device 200 calculating the differencebetween the objective function values and using this value as the costof lost opportunity or other impact for the inferential quality issue.

If another process variable remains to be processed at step 618, themethod returns to step 608 to select and process another processvariable. The final results from the method 600 include anidentification of impacts caused by inferential quality issuesassociated with inferred properties used by a model-based controller106.

FIG. 7 illustrates an example method 700 for estimating impacts ofprocess variable mode issues in advanced control operations according tothis disclosure. The method 700 could, for example, be performed as partof step 306 in FIG. 3. For ease of explanation, the method 700 isdescribed as being performed by the device 200 of FIG. 2 implementing anoperator console 110, server 117, or device used in the computing cloud118 of FIG. 1. However, the method 700 could be used with any suitabledevice and in any suitable system.

As shown in FIG. 7, a control matrix of a model-based controller isobtained at step 702. This could include, for example, the processor 202of the device 200 obtaining the control matrix from any suitablesource(s). The control matrix generally identifies the manipulated andcontrolled variables that the controller 106 is currently able toconsider or use when controlling an industrial process.

Current modes of operation for various process variables associated withthe model-based controller are identified at step 704. This couldinclude, for example, the processor 202 of the device 200 determiningwhether certain process variables are currently being considered by thecontroller 106 in controlling the industrial process or have beendropped from consideration (such as by an operator). When a processvariable is currently being used by the controller 106, this could alsoinclude the processor 202 of the device 200 identifying the actualoperating mode for the process variable. Example operating modes couldinclude a manual mode where the process variable is manually controlledby an operator, an automatic mode where the process variable iscontrolled by the controller, or a cascade mode (similar to automaticmode except the process variable's setpoint comes from an externalsource like another controller). This information can be relevant sincesome process variables may actually be designed to be dropped in certainmodes of operation. This data could be obtained from any suitablesource(s), such as directly from the model-based controller 106 orindirectly, such as via a historian 114, server 116, or gateway 120.

The process variables that have been dropped from the control matrix areidentified at step 706. This could include, for example, the processor202 of the device 200 identifying any process variables that thecontroller 106 was previously configured to use and that are notcurrently in the controller's control matrix. One of the dropped processvariable is selected at step 708 and inserted back into the controller'scontrol matrix at step 710. This could include, for example, theprocessor 202 of the device 200 selecting one of the dropped processvariables and modifying the control matrix to indicate that the selectedprocess variable is to be used to control the industrial process. A newvalue of the controller's objective function is calculated at step 712.This could include, for example, the processor 202 of the device 200using the shadow optimizer to calculate the value of the controller'sobjective function using the modified control matrix. A differencebetween the new value of the controller's objective function and a priorvalue of the controller's objective function is identified at step 714,and the difference is used as the impact caused by a process variablemode issue at step 716. This could include, for example, the processor202 of the device 200 calculating the difference between the objectivefunction values and using this value as the cost of lost opportunity orother impact for the process variable mode issue.

If another dropped process variable remains to be processed at step 718,the method returns to step 708 to select and process another droppedprocess variable. The final results from the method 700 include anidentification of impacts caused by process variable mode issuesassociated with process variables that have been dropped fromconsideration by a model-based controller 106.

Although FIGS. 4A through 7 illustrate example methods for performingoperations in the method 300 of FIG. 3, various changes may be made toFIGS. 4A through 7. For example, while shown as a series of steps,various steps in each figure could overlap, occur in parallel, occur ina different order, or occur any number of times. Also, while specificoperations and calculations have been described here, these relate tospecific implementations of the operations in the method 300 of FIG. 3.Those operations in FIG. 3 could be performed in any other suitablemanner.

Note that the techniques for identifying impacts of operational problemsin advanced control operations described above could be used or operatein conjunction with any combination or all of various features describedin the provisional patent applications incorporated by reference aboveand/or in the following concurrently-filed patent applications (all ofwhich are hereby incorporated by reference):

U.S. patent application Ser. No. ______, [Docket No. H0060235-0114]entitled “APPARATUS AND METHOD FOR AUTOMATED IDENTIFICATION ANDDIAGNOSIS OF CONSTRAINT VIOLATIONS”;

U.S. patent application Ser. No. ______, [Docket No. H0060315-0114]entitled “APPARATUS AND METHOD FOR IDENTIFYING IMPACTS AND CAUSES OFVARIABILITY OR CONTROL GIVEAWAY ON MODEL-BASED CONTROLLER PERFORMANCE”;and

U.S. patent application Ser. No. ______, [Docket No. H0060410-0114]entitled “APPARATUS AND METHOD FOR IDENTIFYING, VISUALIZING, ANDTRIGGERING WORKFLOWS FROM AUTO-SUGGESTED ACTIONS TO RECLAIM LOSTBENEFITS OF MODEL-BASED INDUSTRIAL PROCESS CONTROLLERS”.

In some embodiments, various functions described in this patent documentare implemented or supported by a computer program that is formed fromcomputer readable program code and that is embodied in a computerreadable medium. The phrase “computer readable program code” includesany type of computer code, including source code, object code, andexecutable code. The phrase “computer readable medium” includes any typeof medium capable of being accessed by a computer, such as read onlymemory (ROM), random access memory (RAM), a hard disk drive, a compactdisc (CD), a digital video disc (DVD), or any other type of memory. A“non-transitory” computer readable medium excludes wired, wireless,optical, or other communication links that transport transitoryelectrical or other signals. A non-transitory computer readable mediumincludes media where data can be permanently stored and media where datacan be stored and later overwritten, such as a rewritable optical discor an erasable storage device.

It may be advantageous to set forth definitions of certain words andphrases used throughout this patent document. The terms “application”and “program” refer to one or more computer programs, softwarecomponents, sets of instructions, procedures, functions, objects,classes, instances, related data, or a portion thereof adapted forimplementation in a suitable computer code (including source code,object code, or executable code). The term “communicate,” as well asderivatives thereof, encompasses both direct and indirect communication.The terms “include” and “comprise,” as well as derivatives thereof, meaninclusion without limitation. The term “or” is inclusive, meaningand/or. The phrase “associated with,” as well as derivatives thereof,may mean to include, be included within, interconnect with, contain, becontained within, connect to or with, couple to or with, be communicablewith, cooperate with, interleave, juxtapose, be proximate to, be boundto or with, have, have a property of, have a relationship to or with, orthe like. The phrase “at least one of,” when used with a list of items,means that different combinations of one or more of the listed items maybe used, and only one item in the list may be needed. For example, “atleast one of: A, B, and C” includes any of the following combinations:A, B, C, A and B, A and C, B and C, and A and B and C.

The description in the present application should not be read asimplying that any particular element, step, or function is an essentialor critical element that must be included in the claim scope. The scopeof patented subject matter is defined only by the allowed claims.Moreover, none of the claims invokes 35 U.S.C. § 112(f) with respect toany of the appended claims or claim elements unless the exact words“means for” or “step for” are explicitly used in the particular claim,followed by a participle phrase identifying a function. Use of termssuch as (but not limited to) “mechanism,” “module,” “device,” “unit,”“component,” “element,” “member,” “apparatus,” “machine,” “system,”“processor,” or “controller” within a claim is understood and intendedto refer to structures known to those skilled in the relevant art, asfurther modified or enhanced by the features of the claims themselves,and is not intended to invoke 35 U.S.C. § 112(f).

While this disclosure has described certain embodiments and generallyassociated methods, alterations and permutations of these embodimentsand methods will be apparent to those skilled in the art. Accordingly,the above description of example embodiments does not define orconstrain this disclosure. Other changes, substitutions, and alterationsare also possible without departing from the spirit and scope of thisdisclosure, as defined by the following claims.

What is claimed is:
 1. A method comprising: obtaining data associatedwith operation of a model-based industrial process controller;identifying at least one estimated impact of at least one operationalproblem of the industrial process controller, each estimated impactexpressed in terms of a lost opportunity associated with operation ofthe industrial process controller; and presenting the at least oneestimated impact to a user.
 2. The method of claim 1, wherein: the atleast one estimated impact comprises impacts associated with noise orvariance in process variables used by the industrial process controller;and identifying the at least one estimated impact comprises: for each ofthe process variables, (i) changing a current average value of theprocess variable to a steady-state value of the process variable and(ii) calculating a new value of an objective function associated withthe controller using the changed average value of the process variable;using a difference between a first of the new values of the objectivefunction and a prior value of the objective function as the impactassociated with noise or variance in one of the process variables; andusing changes between the new values of the objective function as theimpacts associated with noise or variance in others of the processvariables.
 3. The method of claim 1, wherein: the at least one estimatedimpact comprises an impact associated with misconfiguration of anoptimizer in the industrial process controller; and identifying the atleast one estimated impact comprises: changing current average values ofmultiple process variables used by the industrial process controller tosteady-state values of the process variables; calculating a new value ofan objective function associated with the controller using the changedaverage values of the process variables; and using a difference betweenthe new value of the objective function and a prior value of theobjective function as the impact associated with the misconfiguration ofthe optimizer.
 4. The method of claim 1, wherein: the at least oneestimated impact comprises impacts associated with one or more limits onone or more process variables used by the industrial process controller;and identifying the at least one estimated impact comprises, for eachlimit: changing a current value of the limit to an ideal value;calculating a new value of an objective function associated with thecontroller using the changed value of the limit; and using a differencebetween the new value of the objective function and a prior value of theobjective function as the impact associated with the limit.
 5. Themethod of claim 1, wherein: the at least one estimated impact comprisesan impact associated with a quality of at least one model used by theindustrial process controller; and identifying the at least oneestimated impact comprises: identifying a model error associated withthe at least one model; calculating a new value of an objective functionassociated with the controller using corrected gains or gain multipliersassociated with the model error; and using a difference between the newvalue of the objective function and a prior value of the objectivefunction as the impact associated with the quality of the at least onemodel.
 6. The method of claim 1, wherein: the at least one estimatedimpact comprises impacts associated with a quality of one or moreinferred properties used by the industrial process controller; andidentifying the at least one estimated impact comprises: identifying atleast one statistic of differences between predicted and laboratoryvalues for the one or more inferred properties; and for each of multipleprocess variables used by the industrial process controller, (i)changing the process variable by a shift amount based on the at leastone statistic, (ii) calculating a new value of an objective functionassociated with the controller using the changed value of the processvariable, and (iii) using a difference between the new value of theobjective function and a prior value of the objective function as theimpact of the process variable on the quality of the one or moreinferred properties.
 7. The method of claim 1, wherein: the at least oneestimated impact comprises at least one impact associated with one ormore process variables being dropped from use by the industrial processcontroller; and identifying the at least one estimated impact comprises,for each of the one or more process variables: adding the processvariable to a control matrix associated with the controller; calculatinga new value of an objective function associated with the controllerusing the control matrix; and using a difference between the new valueof the objective function and a prior value of the objective function asthe impact of the process variable being dropped from use by theindustrial process controller.
 8. The method of claim 1, wherein:multiple estimated impacts are identified; and at least some of theestimated impacts are presented in a ranked order.
 9. An apparatuscomprising: at least one interface configured to receive data associatedwith operation of a model-based industrial process controller; and atleast one processor configured to: identify at least one estimatedimpact of at least one operational problem of the industrial processcontroller, each estimated impact expressed in terms of a lostopportunity associated with operation of the industrial processcontroller; and present the at least one estimated impact to a user. 10.The apparatus of claim 9, wherein: the at least one estimated impactcomprises impacts associated with noise or variance in process variablesused by the industrial process controller; and to identify the at leastone estimated impact, the at least one processor is configured to: foreach of the process variables, (i) change a current average value of theprocess variable to a steady-state value of the process variable and(ii) calculate a new value of an objective function associated with thecontroller using the changed average value of the process variable; usea difference between a first of the new values of the objective functionand a prior value of the objective function as the impact associatedwith noise or variance in one of the process variables; and use changesbetween the new values of the objective function as the impactsassociated with noise or variance in others of the process variables.11. The apparatus of claim 9, wherein: the at least one estimated impactcomprises an impact associated with misconfiguration of an optimizer inthe industrial process controller; and to identify the at least oneestimated impact, the at least one processor is configured to: changecurrent average values of multiple process variables used by theindustrial process controller to steady-state values of the processvariables; calculate a new value of an objective function associatedwith the controller using the changed average values of the processvariables; and use a difference between the new value of the objectivefunction and a prior value of the objective function as the impactassociated with the misconfiguration of the optimizer.
 12. The apparatusof claim 9, wherein: the at least one estimated impact comprises impactsassociated with one or more limits on one or more process variables usedby the industrial process controller; and to identify the at least oneestimated impact, the at least one processor is configured, for eachlimit, to: change a current value of the limit to an ideal value;calculate a new value of an objective function associated with thecontroller using the changed value of the limit; and use a differencebetween the new value of the objective function and a prior value of theobjective function as the impact associated with the limit.
 13. Theapparatus of claim 9, wherein: the at least one estimated impactcomprises an impact associated with a quality of at least one model usedby the industrial process controller; and to identify the at least oneestimated impact, the at least one processor is configured to: identifya model error associated with the at least one model; calculate a newvalue of an objective function associated with the controller usingcorrected gains or gain multipliers associated with the model error; anduse a difference between the new value of the objective function and aprior value of the objective function as the impact associated with thequality of the at least one model.
 14. The apparatus of claim 9,wherein: the at least one estimated impact comprises impacts associatedwith a quality of one or more inferred properties used by the industrialprocess controller; and to identify the at least one estimated impact,the at least one processor is configured to: identify at least onestatistic of differences between predicted and laboratory values for theone or more inferred properties; and for each of multiple processvariables used by the industrial process controller, (i) change theprocess variable by a shift amount based on the at least one statistic,(ii) calculate a new value of an objective function associated with thecontroller using the changed value of the process variable, and (iii)use a difference between the new value of the objective function and aprior value of the objective function as the impact of the processvariable on the quality of the one or more inferred properties.
 15. Theapparatus of claim 9, wherein: the at least one estimated impactcomprises at least one impact associated with one or more processvariables being dropped from use by the industrial process controller;and to identify the at least one estimated impact, the at least oneprocessor is configured, for each of the one or more process variables,to: add the process variable to a control matrix associated with thecontroller; calculate a new value of an objective function associatedwith the controller using the control matrix; and use a differencebetween the new value of the objective function and a prior value of theobjective function as the impact of the process variable being droppedfrom use by the industrial process controller.
 16. A non-transitorycomputer readable medium containing instructions that when executedcause at least one processing device to: obtain data associated withoperation of a model-based industrial process controller; identify atleast one estimated impact of at least one operational problem of theindustrial process controller, each estimated impact expressed in termsof a lost opportunity associated with operation of the industrialprocess controller; and present the at least one estimated impact to auser.
 17. The non-transitory computer readable medium of claim 16,wherein: the at least one estimated impact comprises impacts associatedwith noise or variance in process variables used by the industrialprocess controller; and the instructions that when executed cause the atleast one processing device to identify the at least one estimatedimpact comprise instructions that when executed cause the at least oneprocessing device to: for each of the process variables, (i) change acurrent average value of the process variable to a steady-state value ofthe process variable and (ii) calculate a new value of an objectivefunction associated with the controller using the changed average valueof the process variable; use a difference between a first of the newvalues of the objective function and a prior value of the objectivefunction as the impact associated with noise or variance in one of theprocess variables; and use changes between the new values of theobjective function as the impacts associated with noise or variance inothers of the process variables.
 18. The non-transitory computerreadable medium of claim 16, wherein: the at least one estimated impactcomprises an impact associated with misconfiguration of an optimizer inthe industrial process controller; and the instructions that whenexecuted cause the at least one processing device to identify the atleast one estimated impact comprise instructions that when executedcause the at least one processing device to: change current averagevalues of multiple process variables used by the industrial processcontroller to steady-state values of the process variables; calculate anew value of an objective function associated with the controller usingthe changed average values of the process variables; and use adifference between the new value of the objective function and a priorvalue of the objective function as the impact associated with themisconfiguration of the optimizer.
 19. The non-transitory computerreadable medium of claim 16, wherein: the at least one estimated impactcomprises impacts associated with one or more limits on one or moreprocess variables used by the industrial process controller; and theinstructions that when executed cause the at least one processing deviceto identify the at least one estimated impact comprise instructions thatwhen executed cause the at least one processing device to, for eachlimit: change a current value of the limit to an ideal value; calculatea new value of an objective function associated with the controllerusing the changed value of the limit; and use a difference between thenew value of the objective function and a prior value of the objectivefunction as the impact associated with the limit.
 20. The non-transitorycomputer readable medium of claim 16, wherein: the at least oneestimated impact comprises an impact associated with a quality of atleast one model used by the industrial process controller; and theinstructions that when executed cause the at least one processing deviceto identify the at least one estimated impact comprise instructions thatwhen executed cause the at least one processing device to: identify amodel error associated with the at least one model; calculate a newvalue of an objective function associated with the controller usingcorrected gains or gain multipliers associated with the model error; anduse a difference between the new value of the objective function and aprior value of the objective function as the impact associated with thequality of the at least one model.
 21. The non-transitory computerreadable medium of claim 16, wherein: the at least one estimated impactcomprises impacts associated with a quality of one or more inferredproperties used by the industrial process controller; and theinstructions that when executed cause the at least one processing deviceto identify the at least one estimated impact comprise instructions thatwhen executed cause the at least one processing device to: identify atleast one statistic of differences between predicted and laboratoryvalues for the one or more inferred properties; and for each of multipleprocess variables used by the industrial process controller, (i) changethe process variable by a shift amount based on the at least onestatistic, (ii) calculate a new value of an objective functionassociated with the controller using the changed value of the processvariable, and (iii) use a difference between the new value of theobjective function and a prior value of the objective function as theimpact of the process variable on the quality of the one or moreinferred properties.
 22. The non-transitory computer readable medium ofclaim 16, wherein: the at least one estimated impact comprises at leastone impact associated with one or more process variables being droppedfrom use by the industrial process controller; and the instructions thatwhen executed cause the at least one processing device to identify theat least one estimated impact comprise instructions that when executedcause the at least one processing device, for each of the one or moreprocess variables, to: add the process variable to a control matrixassociated with the controller; calculate a new value of an objectivefunction associated with the controller using the control matrix; anduse a difference between the new value of the objective function and aprior value of the objective function as the impact of the processvariable being dropped from use by the industrial process controller.